Open Access Open Access  Restricted Access Subscription or Fee Access

A Survey on Ant Colony Optimization Algorithm for Travelling Salesman Problem

Ranjeet Savita, Pankaj Sharma, Manish Gupta

Abstract


The ant colony optimization algorithm, abbreviated as ACO, is a meta-heuristic optimization algorithm which is based on the probability for solving different computational problems such as travelling salesman problem (TSP), job scheduling problem, vehicle routing problem etc. This algorithm is a member of ant colony algorithms family in swarm intelligence methods, which is based on the foraging behaviour of real ants. This paper presents a review on a variety of modified versions of ant colony optimization algorithms for solving travelling salesman problem. This work is helpful for a variety of researchers to solve TSP problem using a variety of modified versions of ACO algorithms.

 


Full Text:

PDF

References


Colorni A, Dorigo M, Maniezzo V. Distributed optimization by ant colonies, Proceedings of European Conference on Artificial Life, Paris, France, 134–142p, 1991.

Jakob V, Ren T. A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. Proceedings of Congress on Evolutionary Computation, Poland, 2004; 2: 1980–1987p.

X-song, LI B, YANG H. Improved Ant Colony Algorithm and Its Applications in TSP, Proceedings of Sixth International Conference on Intelligent Systems Design and Applications (ISDA’06), IEEE, 2006.

Stutzle T. Algorithm for, H.H. Hoos, MAX-MIN ant system and local search for the traveling salesman problem, IEEE Int’l Conf. on Evolutionary Computation. Indianapolis: IEEE Press, 1997. 309~314.

Jadon RLS, Datta U. Modified ant colony optimization algorithm with uniform mutation using self-adaptive approach for travelling salesman problem, ICCCNT-2013, IEEE, Tiruchengode, India.

Zhang Y, Pei Z-l, Yang J-h, et al. An Improved Ant Colony Optimization Algorithm Based on Route Optimization and Its Applications in Traveling Salesman Problem, IEEE International Conference on Bioinformatics and Bioengineering, 2007. 1-4244-1509-8.

Zhao F, Dong J, Li S, et al. An improved ant colony optimization algorithm with embedded genetic algorithm for the travelling salesman problem, Proceedings of the 7th World Congress on Intelligent Control and Automation, Chongqing, China, June 25-27, 2008.

Gan R, Guo Q, Chang H, et al. Improved ant colony optimization algorithm for the travelling salesman problems, J Syst Eng Electron. April 2010; 21(2): 329–333p.

Abd Aziz Z. Ant Colony Hyper-heuristics for Travelling Salesman Problem, IEEE International Symposium on Robotics and Intelligent Sensors (IRIS 2015), Procedia Computer Science. 2015; 76: 534–538p.

Shufen L, Huang L, Lu H. Pheromone Model Selection in Ant Colony Optimization for the Travelling Salesman Problem, Chinese J Electr. 26(2): Mar. 2017.


Refbacks

  • There are currently no refbacks.